Guardrails for conditional molecular generation

Abstract

In various examples, a technique for providing a guardrail for conditional molecular generation includes inputting a latent representation of a molecule generated using a trained generative model during a first generative instance into one or more classifiers. The technique also includes generating, via execution of the classifier(s) based on the latent representation, one or more scores, wherein each score represents a predicted measure of a different undesired attribute for the latent representation. The technique further includes determining that the score(s) are not within one or more acceptable ranges and in response to the determination, preventing the trained generative model from generating one or more second latent representations of the molecule, wherein the preventing includes causing the trained generative model to generate, based at least on the score(s), one or more third latent representations associated with the molecule over one or more additional generative instances following the first generative instance.

What is claimed is: 
     
         1 . A method comprising:
 inputting a latent representation of a molecule generated using a trained generative model during a first generative instance into one or more classifiers, wherein each classifier included in the one or more classifiers is trained using training data derived from at least one of one or more molecular dynamics simulations or one or more biological assays;   generating, via execution of the one or more classifiers and based at least on the latent representation of the molecule, one or more scores, wherein each score included in the one or more scores represents a predicted measure of a different undesired attribute for the latent representation of the molecule;   determining that the one or more scores are not within one or more acceptable ranges; and   in response to the determination, preventing the trained generative model from generating one or more second latent representations of the molecule, wherein the preventing comprises causing the trained generative model to generate, based at least on the one or more scores, one or more third latent representations associated with the molecule over one or more additional generative instances following the first generative instance.   
     
     
         2 . The method of  claim 1 , further comprising:
 generating, via execution of the one or more classifiers and based at least on a second latent representation of a second molecule generated using the trained generative model, one or more additional scores; and   outputting the second molecule as a drug candidate based at least on a comparison of the one or more additional scores with one or more thresholds.   
     
     
         3 . The method of  claim 1 , further comprising:
 generating, via execution of the one or more classifiers and based at least on a second latent representation of a second molecule generated using a generative model, one or more additional scores;   computing one or more losses based on the one or more additional scores; and   updating one or more parameters of the generative model based at least on the one or more losses to generate the trained generative model.   
     
     
         4 . The method of  claim 1 , wherein the training data is derived, at least, by:
 performing a first set of molecular dynamics simulations associated with a set of molecules; and   in response to determining that first set of simulation results associated with the first set of molecular dynamics simulations is inconclusive, performing a second set of molecular dynamics simulations, wherein the second set of molecular dynamics simulations is associated with a higher accuracy than the first set of molecular dynamics simulations.   
     
     
         5 . The method of  claim 1 , further comprising generating the latent representation of the molecule, at least, by sampling from a latent space associated with the trained generative model. 
     
     
         6 . The method of  claim 1 , further comprising generating the latent representation of the molecule via a denoising process associated with the trained generative model. 
     
     
         7 . The method of  claim 1 , wherein the causing the trained generative model to generate the one or more third latent representations of the molecule comprises at least one of:
 sampling the one or more third latent representations of the molecule based at least on the one or more scores;   converting the latent representation of the molecule into the one or more third latent representations based at least on the one or more scores; or   conditioning generation of the one or more third latent representations of the molecule by the trained generative model based at least on the one or more scores.   
     
     
         8 . The method of  claim 1 , wherein the one or more classifiers comprise at least one of a tree-based model, a deep learning model, an ensemble model, a transformer neural network, a feedforward neural network, or a graph neural network. 
     
     
         9 . The method of  claim 1 , wherein the trained generative model comprises at least one of a diffusion model, a variational autoencoder, a normalizing flow model, or a generative adversarial network. 
     
     
         10 . The method of  claim 1 , wherein the different undesired attribute is associated with at least one of a toxicity, an illegal substance, a protected substance, or binding to an off-target. 
     
     
         11 . At least one processor comprising:
 processing circuitry to perform operations comprising:
 inputting a latent representation of a molecule generated by a trained generative model during a first generative timestep into one or more classifiers; 
 generating, via execution of the one or more classifiers and based on at least the latent representation of the molecule, one or more scores, wherein each score included in the one or more scores represents a predicted measure of a different undesired attribute for the latent representation of the molecule; 
 determining that the one or more scores are not within one or more acceptable ranges; and 
 in response to the determination, preventing the trained generative model from generating one or more additional latent representations of the molecule, wherein the preventing comprises modifying, based at least on the one or more scores, generation associated with the one or more additional latent representations of the molecule by the trained generative model over one or more additional generative timesteps following the first generative timestep. 
   
     
     
         12 . The at least one processor of  claim 11 , wherein the operations further comprise:
 generating, via execution of the one or more classifiers based at least on a second latent representation of a second molecule generated by the trained generative model, one or more additional scores; and   outputting the second molecule as a drug candidate based on a comparison of the one or more additional scores with one or more thresholds.   
     
     
         13 . The at least one processor of  claim 12 , wherein the second latent representation is generated by the trained generative model based on at least one of a prompt, a sequence of characters, a graph, an image, an additional latent representation, or a three-dimensional (3D) representation. 
     
     
         14 . The at least one processor of  claim 11 , wherein the operations further comprise:
 generating, via execution of the one or more classifiers based at least on a second latent representation of a second molecule generated by a generative model, one or more additional scores;   computing one or more losses based on the one or more additional scores; and   training the generative model based at least on the one or more losses to generate the trained generative model.   
     
     
         15 . The at least one processor of  claim 11 , wherein the one or more classifiers comprise at least one of a tree-based model, a deep learning model, an ensemble model, a transformer neural network, a feedforward neural network, or a graph neural network. 
     
     
         16 . The at least one processor of  claim 11 , wherein the latent representation of the molecule is generated using at least one of:
 a sample from a latent space associated with the trained generative model; or   a denoising process associated with the trained generative model.   
     
     
         17 . The at least one processor of  claim 11 , wherein the predicted measure of the different undesired attribute comprises at least one of a probability of toxicity, a level of toxicity, a similarity to an illegal substance, a similarity to a protected substance, a binding affinity to an off-target, or a likelihood of binding to an off-target. 
     
     
         18 . The at least one processor of  claim 11 , wherein the at least one processor is comprised in at least one of:
 a system for performing simulation operations;   a system for performing digital twin operations;   a system for performing collaborative content creation for 3D assets;   a system for performing one or more deep learning operations;   a system implemented using an edge device;   a system for generating or presenting at least one of virtual reality content, augmented reality content, or mixed reality content;   a system implemented using a robot;   a system for performing one or more conversational AI operations;   a system implemented using one or more large language models (LLMs);   a system implementing one or more vision language models (VLMs);   a system implementing one or more multi modal language models;   a system for generating synthetic data;   a system for performing one or more generative AI operations;   a system incorporating one or more virtual machines (VMs);   a system implemented at least partially in a data center; or   a system implemented at least partially using cloud computing resources.   
     
     
         19 . A system comprising:
 one or more processors to perform operations comprising:
 inputting a latent representation of a molecule generated using a trained generative model into one or more classifiers; 
 generating, based at least on the one or more classifiers processing at least the latent representation of the molecule, one or more scores respectively representing a predicted measure of a different undesired attribute for the latent representation of the molecule; 
 determining that the one or more scores are not within one or more acceptable ranges; and 
 in response to the determination, preventing the trained generative model from generating one or more additional latent representations of the molecule. 
   
     
     
         20 . The system of  claim 19 , wherein the system is comprised in at least one of:
 a system for performing simulation operations;   a system for performing digital twin operations;   a system for performing collaborative content creation for 3D assets;   a system for performing one or more deep learning operations;   a system implemented using an edge device;   a system for generating or presenting at least one of virtual reality content, augmented reality content, or mixed reality content;   a system implemented using a robot;   a system for performing one or more conversational AI operations;   a system implemented using one or more large language models (LLMs);   a system implementing one or more vision language models (VLMs);   a system implementing one or more multi modal language models;   a system for generating synthetic data;   a system for performing one or more generative AI operations;   a system incorporating one or more virtual machines (VMs);   a system implemented at least partially in a data center; or   a system implemented at least partially using cloud computing resources.